Our MapScore paper is now in press at Transactions in GIS! From the abstract:

The MapScore project described here provides a way to evaluate probability maps using actual historical searches. In this work we generated probability maps based on the statistical Euclidean distance tables from ISRID data (Koester, 2008), and compared them to Doke’s (2012) watershed model. Watershed boundaries follow high terrain and may better reflect actual barriers to travel. We also created a third model using the joint distribution using Euclidean and watershed features. On a metric where random maps score 0 and perfect maps score 1, the ISRID Distance Ring model scored 0.78 (95%CI: 0.74-0.82, on 376 cases). The simple Watershed model by itself was clearly inferior at 0.61, but the Combined model was slightly better at .81 (95%CI: 0.77-0.84).

USCG wants a portable infrared WiSAR detector. This RFI was posted on 2-OCT:

The Coast Guard Research and Development Center (RDC) is conducting market research to identify technologies that are suitable for conducting IR searches on foot for persons on frozen waterways. The parameters include detection capabilities of one mile, and recognition capabilities at one-half mile, and identification at approximately one-quarter mile by personnel on foot (monopod is possible). The parameters also include the need to function in extremely cold temperatures, be temporarily submersible, and function regardless of weather conditions or the time of day/night for IR detection.

Thanks very much to summer intern Jonathan Lee (@jonathanlee1) for many MapScore fixes. Jonathan is a keen Python programmer with extra geek points for running Linux on his Macbook Air and having an ASCII-art avatar. He learned his way around Django in no time and brought us a slew of features and code refactoring including: Continue reading "MapScore Updates Summer 2014"

One of our SciCast forecasters posted an excellent analysis of how he estimated the (remaining) chance of success for Bluefin-21 finding MH370 by the end of the question.

Forecast trend for Bluefin-21 success, on SciCast.

Jkominek was wondering why the probability kept jumping up, and created a Bayes Net to argue that there was no good estimation reason for it. (There may be good market reasons -- cashing in to use your points elsewhere.)

People are often incoherent: their probabilities don't add to 100%. We get an 18% gain in accuracy if we coherentize their estimates. But we get a 30% gain in accuracy if we also assign more weight to coherent estimates.

What implications does this have for making subjective probability maps?

The following figure is from a recent paper I co-authored*:

Figure from Karvetski et al. 2014 showing we get more accuracy by ignoring incoherent estimates than by simple unweighted averages. (The unfortunately abbreviated 'BS' means 'Brier Score'. Lower is better, with 0 being perfect.)

What implications does it have for making subjective "consensus" probability maps at the start of a search?